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What is Markov Chain Monte Carlo? MCMC is a computationally expensive iterative technique for sampling from a probability distribution. Basic idea: Construct a Markov Chain such that its stationary distribution is equal to the distribution we wish to sample. After sufficient burn-in time, sampling from the chain is equivalent to sampling from the… (More)

ÐThis paper describes a novel approach to tissue classification using three-dimensional (3D) derivative features in the volume rendering pipeline. In conventional tissue classification for a scalar volume, tissues of interest are characterized by an opacity transfer function defined as a one-dimensional (1D) function of the original volume intensity. To… (More)

This paper presents a vascular representation and segmentation algorithm based on a multiresolution Hermite model (MHM). A two-dimensional Hermite function intensity model is developed which models blood vessel profiles in a quad-tree structure over a range of spatial resolutions. The use of a multiresolution representation simplifies the image modeling and… (More)

A mapping of unit vectors onto a 5D hypersphere is used to model and partition ODFs from HARDI data. This mapping has a number of useful and interesting properties and we make a link to interpretation of the second order spherical harmonic decompositions of HARDI data. The paper presents the working theory and experiments of using a von Mises-Fisher mixture… (More)

The importance of memory performance and capacity is a growing concern for high performance computing laboratories around the world. It has long been recognised that improvements in processor speed exceed the rate of improvement in DRAM memory speed and, as a result, memory access times can be the limiting factor in high performance scientific codes. The… (More)

We describe a new method for visualising tensor fields using a textured mapped volume rendering approach, tensor-splatting. We use an image order method to calculate the 2D Gaussian splats or footprints of the projected 3D Gaussians at an arbitrary number of standard deviations from the centroid. These footprints are then mapped and com-posited front to… (More)

In this paper we focus on using local 3D structure for segmentation. A tensor descriptor is estimated for each neighbourhood, i.e. for each voxel in the data set. The ten-sors are created from a combination of the outputs form a set of 3D quadrature filters. The shape of the tensors describe locally the structure of the neighbourhood in terms of how much it… (More)

A feature selection methodology based on a novel Bhattacharyya Space is presented and illustrated with a texture segmentation problem. The Bhattacharyya Space is constructed from the Bhattacharyya distances of different measurements extracted with sub-band filters from training samples. The marginal distributions of the Bhat-tacharyya Space present a… (More)

This paper addresses the problem of segmenting bone from Computed Tomography (CT) data. In clinical practice, identification of bone is done by thresholding, a method which is simple and fast. Unfortunately , thresholding alone has significant limitations. In particular, segmentation of thin bone structures and of joint spaces is problematic. This problem… (More)